let's say i have a dataframe of 80 columns and 1 target column,
for example a bank account table with 80 attributes for each record (account) and 1 target column which decides if the client stays or leaves.
what steps and algorithms should i follow to select the most effective columns with the higher impact on the target column ?
There are a number of steps you can take, I'll give some examples to get you started:
A correlation coefficient, such as Pearson's Rho (for parametric data) or Spearman's R (for ordinate data).
Feature importances. I like XGBoost for this, as it includes the handy xgb.ggplot.importance / xgb.plot_importance methods.
One of the many feature selection options, such as python's sklearn.feature_selection methods.
This one way to do it using the Pearson correlation coefficient in Rstudio, I used it once when exploring the red_wine dataset my targeted variable or column was the quality and I wanted to know the effect of the rest of the columns on it.
see below figure shows the output of the code as you can see the blue color represents positive relation and red represents negative relations and the closer the value to 1 or -1 the darker the color
c <- cor(
red_wine %>%
# first we remove unwanted columns
dplyr::select(-X) %>%
dplyr::select(-rating) %>%
mutate(
# now we translate quality to a number
quality = as.numeric(quality)
)
)
corrplot(c, method = "color", type = "lower", addCoef.col = "gray", title = "Red Wine Variables Correlations", mar=c(0,0,1,0), tl.cex = 0.7, tl.col = "black", number.cex = 0.9)
Related
I currently have a large data set that is in long format. I am hoping to apply a value only given at baseline (repeat_instance = 0) to all follow up instances (repeat_instance = 1, 2, 3+) based on the record_id.
While I cannot share the actual data I have created a simplified example below to illustrate the quesiton.
record_id <- c(1,1,1,2,3,4,4,5,6,7,8,8,9,10,10,10)
repeat_instance <- c(0,1,2,0,0,0,1,0,0,0,0,1,0,0,1,2)
reason_for_visit <- c(1,NA,NA,1,2,1,NA,1,2,3,1,NA,1,1,NA,NA)
Current Format:
Desired Outcome:
I have seen solutions in Excel, however am not sure which formula may be useful in R.
We can use fill from tidyr
library(tidyr)
fill(df1, reason_for_visit)
data
df1 <- data.frame(record_id, repeat_instance, reason_for_visit)
I am trying to run CBA() classifier (package=arulesCBA) over a dataframe consisting of binary variables (e.g. man/woman) and ordered factors (e.g. age group 1 - 5).
While putting the data.frame with variables as factors and ordered factors as data to CBA(), I get an error:
> Error in discretizeDF.supervised(formula, data, method = disc.method) :
> Cannot discretize non-numeric column: GENAGEGROUPQ2Q8_2Q8_3Q8_4FAM_1FAM_2
When I coerce the data.frame to transactions:
> trans <- transactions(my.dataframe)
...CBA() works nicely but seems that the information about the "order" in ordered factor is lost.
Is there a workaround to keep the information about the order of levels in the ordered factors? Perhaps to treat them as integer (as in the example with iris data)?
Many thanks!
Zdenek Skala
I have a network diagram that looks like this:
I made it using ggraph and added the labels using geom_nodelabel_repel() from ggnetwork:
( ggraph_plot <- ggraph(layout) +
geom_edge_fan(aes(color = as.factor(responses), edge_width = as.factor(responses))) +
geom_node_point(aes(color = as.factor(group)), size = 10) +
geom_nodelabel_repel(aes(label = name, x=x, y=y), segment.size = 1, segment.color = "black", size = 5) +
scale_color_manual("Group", values = c("#2b83ba", "#d7191c", "#fdae61")) +
scale_edge_color_manual("Frequency of Communication", values = c("Once a week or more" = "#444444","Monthly" = "#777777",
"Once every 3 months" = "#888888", "Once a year" = "#999999"),
limits = c("Once a week or more", "Monthly", "Once every 3 months", "Once a year")) +
scale_edge_width_manual("Frequency of Communication", values = c("Once a week or more" = 3,"Monthly" = 2,
"Once every 3 months" = 1, "Once a year" = 0.25),
limits = c("Once a week or more", "Monthly", "Once every 3 months", "Once a year")) +
theme_void() +
theme(legend.text = element_text(size=16, face="bold"),
legend.title = element_text(size=16, face="bold")) )
I want to have the labels on the left side of the plot be off to the left, and the labels on the right side of the plot to be off to the right. I want to do this because the actual labels are quite long (organization names) and they get in the way of the lines in the actual plot.
How can I do this using geom_nodelabel_repel()? i've tried different combinations of box_padding and point_padding, as well as h_just and v_just but these apply to all labels and it doesn't seem like there is a way to subset or position specific points.
Apologies for not providing a reproducible example but I wasn't sure how to do this without compromising the identities of respondents from my survey.
Well, there is always the manually-intensive, yet effective method of separately adding the geom_node_label_repel function for the nodes on the "left" vs. the "right" of the plot. It's not at all elegant and probably bad coding practice, but I've done similar things myself when I can't figure out an elegant solution. It works really well when you don't have a very large dataset to begin with and if you are not planning to make the same plot over and over again. Basically, it would entail:
Identifying if there exists a property in your dataset that places points on the "left" vs. the "right". In this case, it doesn't look like it, so you would just have to create a list manually of those entries on the "left" vs. "right" of your plot.
Using separate calls to geom_node_label_repel with different nudge_x values. Use any reasonable method to subset the "left" and "right datapoints. You can create a new column in the dataset, or use formatting in-line like data = subset(your.data.frame, property %in% left.list)
For example, if you created a column called subset.side, being either "left" or "right" in your data.frame (here: your.data.frame), your calls to geom_node_label_repel might look something like:
geom_node_label_repel(
data=subset(your.data.frame, subset.side=='left'),
aes(label=name, x=x, y=y), segment.size=1, segment.color='black', size=5,
nudge_x=-10
) +
geom_node_label_repel(
data=subset(your.data.frame, subset.side=='right'),
aes(label=name, x=x, y=y), segment.size=1, segment.color='black', size=5,
nudge_x=10
) +
Alternatively, you can create a list based on the label name itself--let's say you called those lists names.left and names.right, where you can subset accordingly by swapping in as represented in the pseudo code below:
geom_node_label_repel(
data=subset(your.data.frame, name %in% names.left),...
nudge_x = -10, ...
) +
geom_node_label_repel(
data=subset(your.data.frame, name %in% names.right),...
nudge_x = 10, ...
)
To be fair, I have not worked with the node geoms before, so I am assuming here that the positioning of the labels will not affect the mapping (as it would not with other geoms).
How can I easily compare the distributions of multiple cohorts?
Usually, https://seaborn.pydata.org/generated/seaborn.distplot.html would be a great tool to visually compare distributions. However, due to the size of my dataset, I needed to compress it and only keep the counts.
It was created as:
SELECT age, gender, compress_distributionUDF(collect_list(struct(target_y_n, count, distribution_value))) GROUP BY age, gender
where compress_distributionUDF simply takes a list of tuples and returns the counts per group.
This leaves me with a list of
Row(distribution_value=60.0, count=314251, target_y_n=0)
nested inside a pandas.Series, but one per each chohort.
Basically, it is similar to:
pd.DataFrame({'foo':[1,2], 'bar':['first', 'second'], 'baz':[{'target_y_n': 0, 'value': 0.5, 'count':1000},{'target_y_n': 1, 'value': 1, 'count':10000}]})
and I wonder how to compare distributions:
within a cohort 0 vs. 1 of target_y_n
over multiple cohorts
in a way which is visually still understandable and not only a mess.
edit
For a single cohort Plotting pre aggregated data in python could be the answer, but how can multiple cohorts be compared (not just in a loop) as this leads to too many plots to compare?
I am still quite confused but we can start from this and see where it goes. From your example, I am focusing on baz as it is not clear to me what foo and bar are (I assume cohorts).
So let focus on baz and plot the different distributions according to target_y_n.
sns.catplot('value','count',data=df, kind='bar',hue='target_y_n',dodge=False,ci=None)
sns.catplot('value','count',data=df, kind='box',hue='target_y_n',dodge=False)
plt.bar(df[df['target_y_n']==0]['value'],df[df['target_y_n']==0]['count'],width=1)
plt.bar(df[df['target_y_n']==1]['value'],df[df['target_y_n']==1]['count'],width=1)
plt.legend(['Target=0','Target=1'])
sns.barplot('value','count',data=df, hue = 'target_y_n',dodge=False,ci=None)
Finally try to have a look at the FacetGrid class to extend your comparison (see here).
g=sns.FacetGrid(df,col='target_y_n',hue = 'target_y_n')
g=g.map(sns.barplot,'value','count',ci=None)
In your case you would have something like:
g=sns.FacetGrid(df,col='target_y_n',row='cohort',hue = 'target_y_n')
g=g.map(sns.barplot,'value','count',ci=None)
And a qqplot option:
from scipy import stats
def qqplot(x, y, **kwargs):
_, xr = stats.probplot(x, fit=False)
_, yr = stats.probplot(y, fit=False)
plt.scatter(xr, yr, **kwargs)
g=sns.FacetGrid(df,col='cohort',hue = 'target_y_n')
g=g.map(qqplot,'value','count')
I have a model that predicts 10 words for a particular course in order of likelihood, and I'd like the first 5 words of those words that appear in the course's description.
This is the format of the data:
course_name course_title course_description predicted_word_10 predicted_word_9 predicted_word_8 predicted_word_7 predicted_word_6 predicted_word_5 predicted_word_4 predicted_word_3 predicted_word_2 predicted_word_1
Xmath 32 Precalculus Polynomial and rational functions, exponential... directed scholars approach build african different visual cultures placed global
Xphilos 2 Morality Introduction to ethical and political philosop... make presentation weekly european ways general range questions liberal speakers
My idea is for each row to start iterating from predicted_word_1 until I get the first 5 that are in the description. I'd like to save those words in the order they appear into additional columns description_word_1 ... description_word_5. (If there are <5 predicted words in the description I plan to return NAN in the corresponding columns).
To clarify with an example: if the course_description of a course is 'Polynomial and rational functions, exponential and logarithmic functions, trigonometry and trigonometric functions. Complex numbers, fundamental theorem of algebra, mathematical induction, binomial theorem, series, and sequences. ' and its first few predicted words are irrelevantword1, induction, exponential, logarithmic, irrelevantword2, polynomial, algebra...
I would want to return induction, exponential, logarithmic, polynomial, algebra for that in that order and do the same for the rest of the courses.
My attempt was to define an apply function that will take in a row and iterate from the first predicted word until it finds the first 5 that are in the description, but the part I am unable to figure out is how to create these additional columns that have the correct words for each course. This code will currently only keep the words for one course for all the rows.
def find_top_description_words(row):
print(row['course_title'])
description_words_index=1
for i in range(num_words_per_course):
description = row.loc['course_description']
word_i = row.loc['predicted_word_' + str(i+1)]
if (word_i in description) & (description_words_index <=5) :
print(description_words_index)
row['description_word_' + str(description_words_index)] = word_i
description_words_index += 1
df.apply(find_top_description_words,axis=1)
The end goal of this data manipulation is to keep the top 10 predicted words from the model and the top 5 predicted words in the description so the dataframe would look like:
course_name course_title course_description top_description_word_1 ... top_description_word_5 predicted_word_1 ... predicted_word_10
Any pointers would be appreciated. Thank you!
If I understand correctly:
Create new DataFrame with just 100 predicted words:
pred_words_lists = df.apply(lambda x: list(x[3:].dropna())[::-1], axis = 1)
Please note that, there are lists in each row with predicted words. The order is nice, I mean the first, not empty, predicted word is on the first place, the second on the second place and so on.
Now let's create a new DataFrame:
pred_words_df = pd.DataFrame(pred_words_lists.tolist())
pred_words_df.columns = df.columns[:2:-1]
And The final DataFrame:
final_df = df[['course_name', 'course_title', 'course_description']].join(pred_words_df.iloc[:,0:11])
Hope this works.
EDIT
def common_elements(xx, yy):
temp = pd.Series(range(0, len(xx)), index= xx)
return list(df.reindex(yy).sort_values()[0:10].dropna().index)
pred_words_lists = df.apply(lambda x: common_elements(x[2].replace(',','').split(), list(x[3:].dropna())), axis = 1)
Does it satisfy your requirements?
Adapted solution (OP):
def get_sorted_descriptions_words(course_description, predicted_words, k):
description_words = course_description.replace(',','').split()
predicted_words_list = list(predicted_words)
predicted_words = pd.Series(range(0, len(predicted_words_list)), index=predicted_words_list)
predicted_words = predicted_words[~predicted_words.index.duplicated()]
ordered_description = predicted_words.reindex(description_words).dropna().sort_values()
ordered_description_list = pd.Series(ordered_description.index).unique()[:k]
return ordered_description_list
df.apply(lambda x: get_sorted_descriptions_words(x['course_description'], x.filter(regex=r'predicted_word_.*'), k), axis=1)